Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Secure your autonomous AI agents against manipulation and subverted objectives with expert adversarial testing.
Autonomous agents that manage procurement, customer service, or internal workflows are high-value targets. Without proper defenses, they can be manipulated to leak data, execute unauthorized transactions, or act against your business goals.
Our adversarial testing identifies and hardens the unique attack surfaces of your agentic systems before they are exploited.
MITRE ATLAS.We move beyond theoretical risks to deliver actionable, prioritized remediation. Our engineers provide hardened agent frameworks, runtime monitoring rules, and integration guidance for your AI governance dashboard to ensure continuous protection.
Explore our broader approach to securing AI systems through our AI Red Teaming and Adversarial Defense pillar or learn about securing the data they rely on with RAG System Adversarial Manipulation Testing.
Our defense service delivers concrete security improvements and operational resilience for your autonomous AI agents, moving beyond theoretical risks to measurable results.
We deliver a detailed inventory of all potential attack vectors specific to your agent's architecture, including tool misuse, memory corruption, and external API manipulation. This actionable map prioritizes remediation based on exploit likelihood and business impact.
Implementation of runtime monitoring and guardrails that detect and block attempts to subvert the agent's primary objectives. This includes cryptographic verification of critical instructions and anomaly detection in task execution sequences.
Hardening of the agent's tool-calling framework with strict input validation, output sanitization, and permission scoping. We eliminate unsafe tool chaining and enforce least-privilege access to databases and external services.
Deployment of lightweight, production-ready sensors that feed security telemetry into your existing SIEM or SOAR platform (e.g., Splunk, Datadog). Enables real-time detection of novel attack patterns post-deployment.
Hands-on workshops for your AI and engineering teams on secure agent design patterns, common vulnerability pitfalls, and how to interpret and respond to security alerts from the deployed monitoring system.
Our tiered service packages provide a clear path to securing your autonomous AI agents against goal hijacking, prompt injection, and adversarial manipulation, scaling from foundational audits to continuous protection.
| Security Capability | Foundation Audit | Comprehensive Defense | Enterprise Resilience |
|---|---|---|---|
Initial Goal Hijacking Vulnerability Assessment | |||
Multi-Agent Communication Protocol Security Review | |||
Adversarial Simulation (Red Teaming) with MITRE ATLAS | |||
Custom Defense Strategy & Hardening Blueprint | Basic | Detailed | Architecture-Wide |
Tool Usage & API Call Integrity Validation | |||
Continuous Monitoring & Threat Detection Setup | |||
Quarterly Adversarial Simulation Updates | |||
Dedicated Security Engineer Support | Priority Slack | 24/7 On-Call | |
Remediation Guidance & Implementation Support | Documentation | Guided Sessions | Hands-On Engineering |
Typical Engagement Timeline | 2-3 weeks | 4-6 weeks | Ongoing Program |
Starting Investment | From $15K | From $45K | Custom Quote |
Our AI Agent Goal Hijacking Defense services are engineered for high-stakes environments where autonomous AI decisions directly impact safety, security, and financial integrity. We harden your agentic systems against manipulation across these critical sectors.
A systematic, expert-led approach to identify and remediate critical vulnerabilities in your autonomous AI agents.
We execute a proven, four-phase methodology to secure your agentic systems against goal hijacking and manipulation. This process delivers actionable threat models, validated attack vectors, and hardened production agents.
successful hijack rate and mean time to compromise.Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Get specific answers about our security assessment process, timeline, and outcomes for protecting autonomous AI agents from adversarial manipulation.
We employ a structured, intelligence-led approach based on the MITRE ATLAS framework. Our process begins with threat modeling specific to your agent's architecture and objectives. We then execute systematic adversarial simulations, including prompt injection, tool corruption, environment manipulation, and multi-agent coordination attacks. Testing covers the full agent lifecycle: planning, execution, memory, and tool usage. Findings are documented with reproducible proof-of-concept exploits and prioritized by risk severity and business impact.
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
The first call is a practical review of your use case and the right next step.
